You're absolutely right that the environmental impact depends on the source of the energy, and less obviously, by the displaced demand that now has to seek energy from less clean sources. Ideally we should have lots of clean energy, but unfortunately we often don't, and even when AI uses clean sources, they're often just forcing preexisting load elsewhere. If we can start investing in power infrastructure projects at the national (or state/province level) then maybe it wouldn't be so bad, but it never happens at a scale that we need.

I think the argument isn't the environmental impact alone, it's the judgement about the net benefit of both the environmental impact and the product produced. I think the statement is "we spent all this power, and for what? Some cats with tits and an absolutely destroyed labour market. Not worth the cost"
Especially because it's a cost that the users of AI are forcing everyone to pay. Privatize profits, socialize losses, and all that.

You're way overcomplicating how it could be done. The argument is that training takes more energy:

Typically if you have a single cost associated with a service, then you amortize that cost over the life of the service: so you take the total energy consumption of training and divide it by the total number of user-hours spent doing inference, and compare that to the cost of a single user running inference for an hour (which they can estimate by the number of user-hours in an hour divided by their global inference energy consumption for that hour).

If these are "apples to orange" comparisons, then why do people defending AI usage (and you) keep making the comparison?

But even if it was true that training is significantly more expensive that inference, or that they're inherently incomparable, that doesn't actually change the underlying observation that inference is still quite energy intensive, and the implicit value statement that the energy spent isn't worth the affect on society

She could be spiking the camera. Also perspective is hard. Maybe the artist just couldn't make it work and thought "fuck it, close enough, people will understand"

I'm still trying to figure out my network settings so that I can have my IoT one one network while still being able to access my home assistant from the other network.

Unfortunately, my ISP is also my cable company, and I have to use their modem/router combo else the cable boxes won't accept the cable signal. I'm using my own wireless access point (which also doubles as a switch for the handful of Ethernet devices I have), and it can split off a separate SSID, but that's not really doing much.

I guess.

It still smells like an apologist argument to be like "yeah but using it doesn't actually use a lot of power".

I'm actually not really sure I believe that argument either, through. I'm pretty sure that inference is hella expensive. When people talk about training, they don't talk about the cost to train on a single input, they talk about the cost for the entire training. So why are we talking about the cost to infer on a single input?
What's the cost of running training, per hour? What's the cost of inference, per hour, on a similarly sized inference farm, running at maximum capacity?

I'm not sure that's true, if you look up things like "tokens per kwh" or "tokens per second per watt" you'll get results of people measuring their power usage while running specific models in specific hardware. This is mainly for consumer hardware since it's people looking to run their own AI servers who are posting about it, but it sets an upper bound.

The AI providers are right lipped about how much energy they use for inference and how many tokens they complete per hour.

You can also infer a bit by doing things like looking up the power usage of a 4090, and then looking at the tokens per second perf someone is getting from a particular model on a 4090 (people love posting their token per second performance every time a new model comes out), and extrapolate that.

I'm interpreting your statement as "the damage is done so we might as well use it"
And I'm saying that using it causes them to train more AIs, which causes more damage.

He isn't talking about locally, he is talking about what it takes for the AI providers to provide the AI.

To say "it takes more energy during training" entirely depends on the load put on the inference servers, and the size of the inference server farm.

Right, but that's kind of like saying "I don't kill babies" while you use a product made from murdered baby souls. Yes you weren't the one who did it, but your continued use of it caused the babies too be killed.

There is no ethical consumption under capitalism and all that, but I feel like here is a line were crossing. This fruit is hanging so low it's brushing the grass.

It's like "sending"

Could be good or bad depending on the context

Enjoy my last day on earth with a citronella candle up my ass

Don't
Worry
About
The
Squirting